Stroke is a leading cause of mortality, morbidity, and healthcare costs in the U.S., but there are tradeoffs associated with tackling each of these dimensions. For example, expanding screening for stroke-specific risk factors to the general population could identify high-risk individuals who might benefit from intensive stroke prevention measures, such as high-dose medical management or revascularization procedures, but these interventions also carry treatment risks and increase the costs of stroke care. Similarly, quality targets can help providers improve stroke care, but excessive quality measurement can be inefficient. Specifically, the American Stroke Association (ASA) has endorsed 15 key acute stroke performance measures that aim to improve the quality of stroke care, but the inefficient amount of time and money spent on excessive quality measurement could ultimately detract from the positive impact that could be realized by prioritizing a smaller number of the most cost- effective measures. These tradeoffs among long-term health benefits, risks, and costs of stroke prevention or quality improvement strategies can best be quantified using disease simulation modeling techniques and cost-effectiveness analysis. We propose to identify cost-effective stroke policies by developing, validating, and applying simulation modeling. Our study has the following aims: 1) Develop and validate a computer-based stroke policy micro-simulation model. We will model the natural history of stroke disease progression and medical treatments to project lifetime health benefits and healthcare costs accrued for a representative U.S. model population, and validate our model results to publicly available datasets from NHANES and NIH-funded cohort studies; 2) Apply the simulation model to evaluate the cost-effectiveness of stroke prevention policies, focusing on targeted and/or staged screening for carotid stenosis and atrial fibrillation. We will use our model to evaluate targeted and staged screening strategies, in addition to no screening or broad population screening strategies, for these stroke risk factors; 3) Apply the simulation model to evaluate the cost-effectiveness of the ASA?s 15 proposed acute ischemic stroke quality measures. Our goal for this aim is to reduce the complexity of stroke quality measurement by identifying the highest value 3-5 provider benchmarks as defined by cost-effectiveness analysis. Our flexible simulation modeling approach will be able to adapt to changes in stroke prevention and treatment paradigms, a feature which is not possible in longitudinal clinical trial study designs. We expect our proposed research will provide healthcare decision-makers with a set of evidence-based policy actions that will prevent more strokes in the general population and efficiently prioritize the use of life-saving treatments in acute stroke patients, all while curbing cost-ineffective stroke care utilization, which not only wastes money but can cause avoidable harm in patients.